Bibliographic record
Abstract
Around the world in many countries, May is “Nurses Month” and May 12, International Nurses Day (Florence Nightingale's birthday). Thinking about this a few months ago led me to think how little we spend in the pages of this journal and others, on the value of nursing in neurosurgery. The cover this month highlights the partnership of nurses and neurosurgeons from years past and the present. My dear friend Dan Leksell shared this image of his father Lars Leksell performing air ventriculography, while his nurse assists with the imaging. Can we even think about the performance of neurosurgery without nursing support, whether in the office, the research environment, the operating room, the hospital wards, or the rehabilitation centers that often help our patients in their recoveries? Do we just take for granted that complex and challenging surgeries will be followed by excellent nursing care that lasts all night, the next day, and the day after that? Nurses talk to our patients when we are not there, reiterate our messages, and clarify and answer patient's questions. They become as familiar with new surgical techniques as we do, to understand their indications and potential outcomes. Then they work to make those outcomes possible. In the surgical suite, they must become familiar with new devices and do so expeditiously to meet our high standards. It is truly a wonder how over the course of their own career they evolve as we evolve, as our field evolves. Neurosurgical nursing is its own calling. As for any of us, the path is different from person to person. To become involved in the care of patients with challenging and debilitating neurological disorders, in one or more different clinical settings, is a commitment toward excellence. I personally think of our work as more a vocation than a career. We are so fortunate to have nurses in the practice setting who help us bring patients and families to an understanding of their own medical challenges and to help them navigate what is assuredly an anxious time. Their calm, unhurried demeanor can help them through a potentially frustrating interaction with the health care system, lessening worry and sleepless nights, at least to some degree. Nurses on the hospital wards provide the surgical aftercare of recovery, hopefully to a prompt return home, but sometimes toward an extended care stay that may develop into more personal contacts with patients, friends, and family. The hands-on, personal interactions involved are truly special and go far beyond the expectations of a “job.” In the surgical suite, our nurses have the same commitment to professionalism, communication, care, and error reduction as we do. This is displayed time and again, each time a new patient enters the room. And last, a personal note. Many years ago now, I married a neurosurgical nurse who had worked on the neurosurgery floor at the Toronto General Hospital and then in the operating room there. She later worked in the OR at the University of Pittsburgh Medical Center. She understood the field, the disorders we treat, and the personalities involved. Her nursing skills were also crucial to our family, including structure, planning, fearlessness, and tenderness. As my dear friend John Golfinos would say, “you can't spell neurosurgery without n-u-r-s-e.” I encourage you to think about the nurses you work with, in any of the different settings, and know that the wonderful science in these journal pages could not have happened without them. Douglas Kondziolka, MD, MSc Editor-in-Chief, Neurosurgery Publications New York, New York, USA
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.052 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.018 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.035 | 0.075 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".